{"id":"W3036174391","doi":"10.1093/gji/ggaa283","title":"3-D joint inversion of airborne gravity gradiometry and magnetic data using a probabilistic method","year":2020,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Memorial University of Newfoundland","funders":"National Natural Science Foundation of China; Department of Natural Resources, Government of Newfoundland and Labrador","keywords":"Covariance matrix; Covariance; Inversion (geology); Geology; Magnetic anomaly; Correlation coefficient; Similarity (geometry); Geophysics; Geodesy; Algorithm; Mathematics; Computer science; Seismology; Statistics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007013558,0.0005819766,0.0004405876,0.0006771239,0.0003297013,0.0008364513,0.0007317878,0.0005102959,0.000833764],"category_scores_gemma":[0.001817228,0.0005493349,0.001120541,0.000686508,0.0006110278,0.0006605522,0.001105282,0.0006903737,0.0002990134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004807177,"about_ca_system_score_gemma":0.00180994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01576058,"about_ca_topic_score_gemma":0.01855445,"domain_scores_codex":[0.999621,0.00008276011,0.00002049261,0.00006695889,0.0001728553,0.00003579454],"domain_scores_gemma":[0.9994851,0.0002079751,0.00007811571,0.00006714282,0.0001331124,0.0000284446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006011111,0.00002469013,0.001580908,0.00005709571,0.00009235388,0.0001157535,0.00007201258,0.913233,0.01287571,0.008969823,0.0005924186,0.06232606],"study_design_scores_gemma":[0.000005451713,0.000007435636,0.0003710509,0.000002755741,0.00000662193,0.00002438321,0.00000599258,0.9966493,0.0008459521,0.001591665,0.000479649,0.000009699626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00799373,0.00003280459,0.9911337,0.00004923815,0.000008574777,0.00001251276,0.0000550697,0.0002833625,0.0004309285],"genre_scores_gemma":[0.4268127,0.00015029,0.5706151,0.00006831205,0.00005474118,0.0001162334,0.0004145261,0.0001465323,0.001621498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01576058,"threshold_uncertainty_score":0.03133768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07729236378754945,"score_gpt":0.3020623754424229,"score_spread":0.2247700116548734,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}